Suchergebnisse

ObjectiveThe Cohort Builder package aims to streamline the cohort building process using R, with simplicity and efficiency as the primary focus. Researchers can generate cohorts from TRE (Trusted Research Environment) datasets by specifying simple cohort definitions as YAML (YAML Ain't a Markup Language) parameters using this package, eliminating the need to write complex queries.
ApproachCohort Builder offers researchers a user-friendly interface for defining cohort criteria by abstracting away the complexities of database queries using YAML configuration files. The package enables researchers to focus on defining the desired cohort characteristics rather than navigating the database and tables. Users can easily specify inclusion and exclusion criteria, demographic constraints, temporal constraints, and other dataset parameters to create cohorts tailored to their research goals and reproduce the results using the same configuration file.
ResultUsing the Cohort Builder, users may quickly create cohorts from TRE datasets with minimum code. The package facilitates reproducibility, transparency, and increased productivity. Researchers gain from having to rely less on specialised database expertise and having more flexibility when defining and exploring cohorts.
ConclusionCohort Builder offers a straightforward yet effective method for defining cohorts. The package frees researchers from having to worry about intricate technological details in favour of their analytical objectives.
ImplicationsMoving forward, Cohort Builder will support creative research projects across a range of disciplines and promote collaboration among researchers by allowing them to share configuration files for their cohorts.

ObjectiveThe Welsh Longitudinal General Practice (WLGP) dataset contains over 4 billion records. Due to the size and the need to filter the dataset to get the necessary general practice interaction results, query performance is poor. To overcome this, a 'cleaned' version of the dataset needed to be created.
ApproachAn R package was created that would be run when a new refresh of the WLGP data is provided. Two new tables would be created based on the original WLGP Events table.
ResultsA WLGP Cleaned Dataset R Package was produced and creates a reformatted events table and events look up table. The reformatted GP events table reduces the original events table from 14 columns to 8 key columns. The events look up table takes distinct events from the original WLGP events table and produces a new table including columns such as the event description, type and hierarchy levels. Both tables are then linked using an event code ID.
ConclusionThe R package now creates a new reformatted events table as well as an events look up table which is run after a WLGP refresh is provided. The new tables are then provisioned to any projects that have access to the dataset.
ImplicationsThe new tables has improved query performance for our researchers, providing them with a table with all the necessary information and an easy way to decipher events codes. This has improved the amount of time researchers have spent manipulating and querying the tables.

ObjectiveWe're introducing a comprehensive approach to enhance sensitive data flagging within our Data Quality (DQ) tool. Within de-identified health records, sensitive data such as user ids, post codes, phone numbers, and addresses pose significant privacy risks if exposed in their raw form. To address this challenge, we propose using all necessary regex patterns and free text field checks for sensitive data flagging with the objective of efficient detection within large datasets and ultimately reporting such data.
ApproachA regex map has been developed along free text field thresholds, distribution counts and percentage checks to allow a generic approach. This ensures that any new regexes can be added to the flagging process without significant code changes.
Once identified, sensitive data instances, including fields that contain free text are flagged and displayed in an html report that is human readable by DQ reviewers.
ResultsThrough a combination of SQL and R regex based text processing, our approach allows a seamless identification and flagging of sensitive data within datasets. Sensitive data for large tables of +25 million records with over 50 columns are getting flagged in less than 150 seconds (approx. 2 minutes).
ConclusionOur development offers a practical solution for sensitive data flagging in complex datasets where team members can implement robust sensitive data flagging by adding more regex formulas in the codebase.
ImplicationsDQ reviewers can inspect the sensitive flagged fields in a human readable way, providing an extra measure of confidence when determining the quality of de-identified health records.

ObjectivesEducation outcomes predict life chances; however disabilities and concurrent learning difficulties are often barriers to school performance. Low educational achievement is also linked with socio-economic deprivation. We aimed to quantify the association of these factors with education outcomes by ages 12 and 17 for children in Wales (UK).
ApproachWe linked health, socio-economic and education data for children born after 1997 in Wales, registered with any one of 17 isolated congenital anomalies. Children were included if they had known education outcomes at ages 12 (n=9,223) and 17 (n=4524). A reference population comprised children born similarly but without anomalies, also having education records at 12 (n=261,827) and 17 (n=4,524).
Multinomial logistic regressions determined the likelihood of attainment in compulsory subjects by age 12 and of attaining five GCSE qualifications by age 17. We adjusted for socio-economic status by using free school meal eligibility (household deprivation) and area-based indices of deprivation.
ResultsWe identified 14 and 11 isolated anomalies that reduced the likelihood of achieving educational standards by ages 12 and 17 respectively. Educational achievements were lower for children with Down syndrome, Turner syndrome, hydrocephalus and limb reduction defects, particularly at age 12, indicating that children with anomalies may catch-up with their peers. While children with anomalies were less likely to achieve five GCSEs by age 17, there was no difference in the grades of those who did so. Deprivation, particularly at household level, further reduced the likelihood of education success. At age 17, household deprivation reduced 3-fold the odds of achieving any five GCSEs grades A*-C.
ConclusionsChildren with congenital anomalies from the most deprived communities and households are at particular risk of low education achievement. To realise their potential, these children should be identified from medical records at an early age and offered targeted educational support as soon as possible and at every opportunity.

ObjectiveThe HDRUK Phenotype Library (phenotypes.healthdatagateway.org) shares definitions used to measure concepts of interest (such as diagnoses or treatments) within health datasets. It already holds more than 1000 phenotypes, with researchers able to contribute their work via an API. We aimed to create a more user-friendly method of contributing to the Library.
ApproachWe designed a phenotype creation workflow enabling users to create and submit new content via web interface. Goals included clarity and ease of use for a broad range of users.
ResultsA home page shows researchers' own content. Researchers can create new phenotypes using a web form, entering metadata such as name, authors, description, and publications. Code lists are defined via one or more rules, including search terms or referring to another phenotype, or by CSV upload. Users can make their phenotypes accessible to a research group or to all authenticated users, as well as publish content on the web. Publication requests are reviewed to ensure content is complete and appropriate. Editing, with full history and version control, is also supported.
ConclusionsWe implemented and released the new features. We are currently engaging researchers to get feedback and invite content submission.
ImplicationsThe benefit of tools to support research transparency and repeatability is only realized when they are adopted. We hope that a GUI to support phenotype creation will broaden the Library's user base help it serve as an enabler of higher-quality, more efficient research across the worldwide health data research community.